Bibliographic record
Abstract
Graphical user interfaces to R The R Commander ( Rcmdr ) will be the main focus, with brief reference to other GUIs. Subsection 1.1.1 mentioned the usefulness of the R Commander for data input. Especially for novices or infrequent users of R, a GUI can be similarly helpful for creating simple graphs, for statistical testing, for simple tabulation and summarization, and for fitting standard models. The final subsection describes how the function gui() ( fgui package) can be used to create simple GUIs. Some tasks are best done from the command line, and some from a GUI, with the balance likely to change in favor of the command line as familiarity with R increases. The two modes of use can be mixed. All the GUIs discussed here make available the commands used by R, for inspection and/or modification and/or for audit trail purposes. The user can examine the help page for the relevant function(s), modify the code as required, and re-execute it. In addition to the R Commander, note: • JGR (Java Graphics for R) and the Deducer GUI that is designed to work with JGR . • The rattle GUI gives access to a range of multivariate graphics, regression and classification routines. • A more limited alternative to the R Commander is pmg (Poor Man's GUI). […]
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.464 | 0.559 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".